Download The Diffusion of Energy Efficiency in Building

The Diffusion of Energy Efficiency in Building
Nils Kok
Maastricht University
Netherlands
[email protected]
Marquise McGraw
University of California
Berkeley
[email protected]
John M. Quigley
University of California
Berkeley
[email protected]
Abstract
Awareness of global warming and the extent of greenhouse gas emissions has
focused more attention upon energy efficiency in building. Moreover, the inventory of
“green” office space in the U.S. has increased dramatically since the introduction of
rating schemes that attest to the energy efficiency or sustainability of commercial
buildings. In some metropolitan areas, the supply of certified office buildings has more
than doubled in the last decade, and there are a few metropolitan areas where “green”
office space now accounts for more than a quarter of the total office stock.
In this paper, we analyze the diffusion of buildings certified for energy efficiency
across US property markets. Using a panel of 48 metropolitan areas (MSAs) observed
over the last 15 years, we trace the diffusion of green building practices across the
country. We then model the geographic patterns and dynamics of building certification,
relating industry composition, changes in economic conditions, characteristics of the
local commercial property market, and the presence of human capital, to the crosssectional variation in energy-efficient building technologies and the diffusion of those
technologies over time. Understanding the determinants and the rate at which energyefficient building practices diffuse over space and time is important for designing policies
to affect resource consumption in the built environment.
December 2010
JEL codes: D12, Q51, R21
Keywords: Innovation, diffusion, energy efficiency, labels, commercial real estate
Prepared for presentation at the Annual Meetings of the American Economic Association,
Denver, Colorado, January 2011.
Financial support for this research was provided by the Royal Institute of Chartered
Surveyors, the European Center for Corporate Engagement, and the University of
California Energy Institute. We are grateful to William Wheaton and also to Jon Southard
of CBRE Econometric Advisors for facilitating access to some of the data used in this
analysis.
1
I. Introduction
There exists an apparently intractable contradiction between the slow diffusion of
energy efficient technologies and the profitability of these measures -- ranging from the
adoption of energy efficient compact fluorescent light bulbs (CFLs) in the residential
sector (Philippe Menanteau and Hervé Lefebvre, 2000) to the replacement of inefficient
heating, ventilation and air conditioning (HVAC) systems in the commercial sector. Early
research on consumer choice suggested that the discount rate applied to more energy
efficient appliances and durable goods was unreasonably high, approaching twenty
percent. (See Jerry A. Hausman, 1979; see also Gilbert E. Metcalf and Kevin A. Hassett,
1999.) This contradiction, termed the “energy paradox” (Adam B. Jaffe and Robert N.
Stavins, 1994), has regained currency in the recent debate on the role of buildings in
carbon reduction and climate change -- as the durability of real capital implies that
existing structures have a substantial impact on life cycle energy consumption (Piet M.A.
Eichholtz, Nils Kok and John M. Quigley, 2010a). Energy consumption in the building
sector has large effects upon greenhouse gas emissions and upon energy use in the
economy.1
Although the slow diffusion of more energy-efficient technologies in buildings is
a widely-discussed challenge to the neoclassical theory of investment -- at least among
engineers (Hunt Alcott and Sendhil Mullainathan, 2010; Stephen J. DeCanio and William
E. Watkins, 1998) -- recent trends suggest that the number of buildings that are labeled as
“energy-efficient,” “sustainable,” or “green,” has surged over the past decade. Energy
certificates for buildings are a testimony to improved building technologies, which are
difficult for laymen to observe. As President Obama put it recently:
“…The Energy Star program [certifying sustainability] was created to
promote energy efficiency by letting consumers know which appliances,
which electronics would save electricity and, therefore, would save them
money over time. The program… applies this concept not only to the
appliances, but also to homes and other buildings -- taking energy
efficiency a step further.”2
1
For example, the construction and operation of buildings account for about a third of world greenhouse
gas emissions, and are responsible for about forty percent of world consumption of raw materials and
energy (RICS, 2005). It has also been emphasized by policy makers that the built environment offers a
great potential for greenhouse gas abatement and energy conservation (e.g., Nicholas Stern, 2008).
2
Speech of President Barack Obama, Savannah Technical College, GA, March 3, 2010.
2
The Energy Star program, administered by the US Environmental Protection
Agency (EPA) and the Department of Energy (DOE), was launched in 1992 as a system
of voluntary labels designed to identify and promote energy-efficient products and home
appliances to conserve energy. The Energy Star label was extended to commercial
buildings in 1995, and the labeling program for these buildings began in 1999. Existing
commercial buildings can receive an Energy Star certification if the source energy use of
the building (that is, the total quantity of energy used in the building, as certified by a
professional engineer) achieves a specified benchmark level;3 the label is awarded to the
top quarter of all comparable buildings, ranked in terms of source energy efficiency. As
measured by program growth, the Energy Star program for commercial buildings appears
to be quite successful -- as of November 2010, some 12,000 commercial buildings had
received the label. More specifically, the number of office buildings certified by the
Energy Star program has increased from a mere 86 (approximately 33 million square
foot) in 1999 to more than 4,400 (approximately 1.3 billion square foot) in 2010.
In a parallel effort, the US Green Building Council (USGBC), a private nonprofit
organization, has developed the Leadership in Environmental and Energy Design (LEED)
green building rating system to encourage the “adoption of sustainable green building and
development practices.” The requirements for the certification of LEED buildings are
substantially more complex than those for the award of an Energy Star rating, and the
certification process measures six distinct components of sustainability, one of which is
energy performance.4 There are four different levels of LEED certification -- certified,
silver, gold and platinum -- and since the start of a single pilot project in 1998, the LEED
system of multiple ratings has become a dominant force in the commercial and
institutional building market in the US. Many states and cities have revised their building
codes to require newly-constructed public buildings to meet LEED performance
standards, and some municipalities include a certain minimum LEED certification for
new commercial construction and for renovations. For example in Atlanta, GA, an
3
The actual source energy consumption of a commercial building is compared to its predicted energy
consumption, based on information collected through CBECS (the Commercial Building Energy
Consumption Survey) and analyzed by DOE.
4
For more information on the rating procedures and measurements, see http://www.usgbc.org/leed.
3
ordinance was promulgated in late 2003 requiring all city-funded projects of more than
5,000 square feet in size, or costing at least $2 million, to obtain a LEED Silver rating.
All new city buildings in Kansas City, MO, are now required to meet criteria for LEED
Silver, and Dallas, TX, requires all city buildings larger than 10,000 square feet to have at
least LEED Silver certification.
Importantly, the expansion of standards for existing structures (e.g., LEED for
Existing Buildings, LEED for Operations and Maintenance), means that the certification
of a “sustainable” building by LEED is no longer solely confined to new construction.
The growth of the LEED program is also substantial, with more than 6,500
commercial buildings (approximately 1 billion square feett) certified for sustainability by
October 2010. Notwithstanding the unprecedented downturn in commercial property
markets in recent years, LEED-certified buildings now account for nearly one-third of
new construction in the U.S, up from two percent in 2005 (McGraw-Hill Construction,
2010). And there is an impressive pipeline of LEED projects, as measured by the current
registration of projects not yet completed -- 27,000 commercial buildings, or 6 billion
square feet of office space.
Presumably, buildings certified for energy efficiency or sustainability incorporate
technologies that systematically reduce resource usage and operating costs. Indeed, the
USGBC claims that LEED-certified buildings not only have lower operating costs but
also provide healthier and safer working environments for occupants. The Energy Star
program asserts that the buildings that have earned its label generally use 35 percent less
energy and emit 35 percent less carbon dioxide than average unlabeled buildings.5 The
investment costs of achieving these benefits and savings are unclear. Although the EPA
and USGBC do not charge directly for providing certification (beyond a nominal
registration fee), the process may involve considerable expenses for property developers
and investors. For instance, the USGBC trains and licenses third-party certification
experts, who charge for their consultancy services. Beyond this, there are incremental
costs associated with the design, material, equipment and construction specifically
tailored to meet LEED guidelines, or to achieve the energy efficiency standards imposed
by Energy Star. Until recently, systematic evidence on the returns to investments in
5
See for more information: http://www.energystar.gov/index.cfm?c=business.bus_index.
4
LEED and Energy Star certification was limited, and it consisted mostly of anecdotal
evidence and industry-initiated case studies.6
There is now a small body of literature that evaluates the importance of these
claims in the marketplace -- mainly focusing on measurable output, rather than noisy
inputs. Increased energy efficiency and other elements related to “sustainability” both
contribute to increases in rents, occupancy rates and asset values in commercial offices
(Piet M.A. Eichholtz, et al., 2010a; Fuerst and MacAllister, 2009, 2011). Moreover,
among rated buildings, incremental energy savings are roughly capitalized into asset
values (Piet M.A. Eichholtz, et al., 2010c).
If the consequences of Energy Star and LEED building practices were transparent,
rational decision-making by investors suggests that the adoption of specific technologies
would be diffused quickly within generic building types and local economies. But there is
substantial dispersion over geography and building types in the diffusion of labeled
buildings. Thus, in common with many other technical innovations, diffusion of energyefficient building practices has varied over time and space. (See Rosenberg, 1976, for an
early discussion of barriers to diffusion.) This variation in diffusion and market
penetration may be explained by expected cost savings from adopting energy efficient
innovations, competitive conditions that affect the appropriability of gains, and
characteristics that influence the expected profitability of the adoption of the innovation
(Nancy L. Rose and Paul L. Joskow, 1990). Of course, institutional characteristics, such
as state or local regulation, may also play an important role in explaining the adoption of
cost-reducing innovations. (Sharon M. Oster and John M. Quigley, 1977; Lori D. Snyder,
Nolan H. Miller and Robert N. Stavins, 2003).
In this paper, we analyze the spread of energy efficient technology in the built
environment. “Technology” is itself difficult to measure, but the labels offer an indirect
approach to assessing the diffusion of improved technology. Even though voluntary
labeling programs do not provide direct measures of energy efficient investments in
6
An exception to anecdotal case studies is the recent report by Davis Langdon, a construction consultancy,
which compares the capital investment in LEED-certified buildings with investments in comparable
buildings. Using a sample of 221 public projects (i.e., schools, hospitals, and libraries), no significant cost
differences are documented (Davis Langdon, 2007).
5
buildings, the decision to label a building may be thought of as a noisy signal of adoption
of more efficient technology and willingness to invest to conserve energy.
This approach has parallels in the more general literature on technology diffusion,
where patents are a widely used proxy for improved technology. (See Wolfgang Keller,
2004, for a recent review.) Using labels as a measure of technology diffusion in building
shares some of same drawbacks: labeling and patenting are conscious choices made by
owners and investors. Some efficient buildings may not be labeled, and some valuable
innovations may not be patented.
Using a detailed panel of 48 MSAs observed annually during a fifteen-year
period, we trace the diffusion of buildings certified for energy efficiency and
sustainability across US metropolitan areas. Then we analyze the geographic patterns and
dynamics and sustainability of building certification, relating industry composition, input
prices and climate, changes in economic conditions, characteristics of the local
commercial property market, and the presence of human capital, to the cross-sectional
and temporal variation and local growth in more efficient office space.
The remainder of this paper is organized as follows. Section II discusses the data
and describes the diffusion of Energy Star and LEED buildings in the US and in a
number of large MSAs. Section III presents data on the economic geography of the
MSAs in our sample, and relates the cross-sectional dispersion in adoption rates to these
measures. We then model the dynamics of diffusion directly. Section IV is a brief
conclusion.
II. The Dynamics of Energy Efficiency in Buildings
As a proxy for the diffusion of energy-efficient technology in building, we use the
certification of Energy Star and LEED building standards across the US. As noted
previously, these systems measure different aspects of “sustainability.” An Energy Star
certification is based solely upon verification of energy use by a professional engineer,
and upon the results of a statistical comparison of usage with “otherwise identical”
buildings by DOE. LEED certification is based upon six criteria for “sustainability,”
6
including one measuring energy use and atmospheric discharges.7 The criteria are hardly
mutually exclusive, and the owners of a number of buildings certified by one program
apply for and receive certification by the other. We specifically focus on commercial
structures rather than public buildings, as investment decision-making for the latter may
arise from motives other than increases in financial returns.
We accessed the data files maintained by the EPA and USGBC and aggregated
the number of buildings and the volume of Energy Star and LEED certified buildings
reported annually by metropolitan area, for the period 1995-August 2010. Figure 1A
presents the growth in energy efficient office space in the US. Clearly the adoption of the
Energy Star certificate is quite a recent phenomenon among commercial office buildings,
with acceleration in growth since 2005. The economic downturn is reflected, perhaps, in
the slower growth rate since 2008 -- an indication of a decline in new construction or a
reduction in energy efficient investments. The second figure reports analogous
information for LEED-certified office space. The market adoption of the LEED system
started somewhat later, but has been increasing rapidly during the past years, without any
apparent slowdown during the recent recession in property markets.
We estimate the importance of energy efficient office space in the private market
using information on the size of commercial property markets across MSAs from CBRE
Econometric Advisors (CBRE-EA).8 This information includes time-series measures of
the stock of space, average contract rents and property prices, and the average vacancy
rates for various property types including office, warehouse, apartment, retail and hotels.
Figure 1B presents the aggregate diffusion curves of Energy Star and LEED
certification for the 48 US metropolitan areas as of October 2010.9 The growth in
7
There is some discussion of the importance of energy efficiency in LEED-certified buildings. A recent
study, based on 100 LEED-certified buildings, finds that LEED buildings use 18–39 percent less energy on
average, per square foot, than their conventional counterparts. Despite this, it was estimated that 28–35
percent of LEED buildings used more energy than their conventional counterparts (Guy R. Newsham et al.,
2009).
8
CBRE Econometric Advisors (CBRE-EA), a subsidiary of CB Richard Ellis, is a major provider of
research services to owners and investors in the U.S. and Canadian commercial real estate markets. We
utilize information from their “Building Stock Database.” For more information see:
https://www.cbre-ea.com.
9
We note that the CBRE Building Stock Database is confined to buildings that are considered
“competitive” -- this criterion is related to building size and differs by market. For example, most markets
have a building size of 10,000 sq. ft. as one of the criteria for “competitive,” but in New York it is higher,
at 50,000 sq. ft. As a result, the estimated fractions of energy efficient space presented in this paper are
7
certified space is the result of three different processes: retrofits of existing buildings, the
installation of more efficient technologies in newly constructed buildings, and the
demolition of obsolete and inefficient buildings. Energy-Star certified buildings are
currently about ten percent of the total office market, but measured by the volume of
space, the fraction is three times as high -- some thirty percent. The apparent relation
between the adoption of energy efficient technology and building size corroborates more
general evidence on technology diffusion; larger companies and production facilities are
more likely to adopt new technologies and to adapt more quickly to changed
circumstances (Stephen J. DeCanio and William E. Watkins, 1998, Lori D. Snyder, et al.,
2003).
The diffusion curve for Energy-Star-labeled space follows the well-documented
S-shaped pattern of innovation diffusion (Zvi Griliches, 1957), although the maximum
adoption rate for Energy Star will never reach 100 percent -- recall that, by design, the
Energy-Star label may be awarded only to the top twenty-five percent of buildings in the
US as ranked by energy efficiency.
The right-hand figure shows that the diffusion of LEED-certified space is still in
early stages, although some five percent of all buildings and eleven percent of the total
volume of office space covered by CBRE-EA had been certified by the LEED label as of
October 2010. The later start of the LEED system and its initial focus on new
construction help explain the relatively slow diffusion rate.
Figure 2 reports the diffusion curves for a selection of US metropolitan areas. The
timing of adoption and growth in space designated as energy efficient differs quite
substantially across metropolitan areas. More than half of the total office stock in Los
Angeles (as monitored by CBRE-EA) has been awarded an Energy Star label, with
labeling starting as early as 1999. In most of the selected MSAs, the fraction of EnergyStar-rated office space is well above twenty percent (with the exception of New York, at
nineteen percent), even though the fraction of certified buildings is roughly ten percent in
most places.
biased upwards, by at least by some small amount. An impediment to time series research on real estate
markets is the lack of reliable, systematic time-series information. To our knowledge, the CBRE database is
the only consistent source of this information.
8
There is also substantial variation between the initial start and the consequent
growth in the diffusion of LEED labels across different markets. Clearly, Chicago and
Phoenix are early adopters of the LEED system (as a fraction of the total market),
whereas the adoption of LEED-certified space started much later in areas like New York
and Dallas. In Section III below, we explore these differences in timing which may be
related to local economic geography, politics, and regulation. In addition, of course, areas
with more recent development are also more likely to have higher fractions of “green”
space.
Appendix Table A1 reports the adoption rates for energy efficient labels for the
twenty-five
largest commercial office markets in 2010. Variations in the market
penetration of sustainable and energy-efficient building technology are substantial.
III. Explaining the Diffusion of Energy Efficiency
A. Data
As noted above, there is a substantial geographic dispersion in the timing and
growth of energy efficient technology as embedded in office buildings. This variation in
diffusion may be explained by: variations across buildings and property markets in the
expected cost savings from adopting energy efficient innovations; variations in local
economic conditions that affect the appropriability of gains; and other characteristics that
influence the expected profitability of the adoption of the energy efficient innovations. Of
course, political and institutional characteristics, such as regulation and ideology, may
also play an important role in explaining the adoption of energy efficient innovations. In
this section, we relate metropolitan-specific variations in industry composition, in
economic conditions, characteristics of the local commercial property market, and the
availability of building professionals (e.g., architects), to the cross-sectional variation and
growth in certified space, using the following measures:
Climatic Conditions. We expect that areas with more adverse climatic conditions
will be more likely to adopt energy-efficient building practices, as the expected economic
9
payoff of these technologies is larger. We employ data on cooling and heating degreedays by MSA as a general proxy for weather.10
Energy Prices. There is a strong presumption, and a modicum of empirical
evidence, that construction practices are more energy-efficient during periods of higher
energy prices (Dora Costa and Matthew E. Kahn, 2010b). We expect that the adoption of
technologies leading to Energy Star and LEED certification increases with the price and
the expected future price of electricity (which is the major part of the energy-mix
consumed in commercial buildings), as the economic return to equivalent investments
improves with higher energy prices, ceteris paribus. Moreover, Energy Star certification
is based directly on the quantities of energy consumed. Assuming a price-elasticity of
energy demand in commercial space that is comparable to residential dwellings, we
would expect significantly lower energy consumption in more expensive areas (Peter C.
Reiss and Matthew W. White, 2008). We exploit average electricity prices by MSA by
year, based on utility data reported by county.11
General Economic Conditions and Industry Composition. The financial payoff
from energy efficiency should be related to conditions in the property market, but moreor-less independent of other general economic conditions in a metropolitan area.
However, it is sometimes argued that “green” is a luxury good, or one which provides a
“warm glow,” and thus the adoption of more sustainable building technologies may be
related to local prosperity (Brian Roe et al., 2001). We employ two measures of income:
per capita personal income and the average wages and salaries reported for the MSA.12
Market demand by firms, industries, and labor for the adoption of energy efficient
innovations may influence the speed of diffusion. Many local jurisdictions have
mandated “green” procurement policies that sometimes include the commercial space
rented by the public sector. We expect a positive relation between the relative size of the
government and the demand for more energy-efficient space. We measure the relative
10
See National Weather Service, National Centers for Environmental Prediction, Climate Prediction
Center, http://www.cpc.noaa.gov/products/analysis_monitoring/cdus/degree_days/
11
See U.S. Energy Information Administration, http://www.eia.doe.gov/cneaf/electricity/page/eia861.html.
Energy prices were constructed using revenue and sales data reported for each utility by the EIA. These
data were mapped to counties and ultimately averaged by MSA (weighted by sales) to derive estimates of
energy prices.
12
Bureau of Economic Analysis, Regional Data Products, Regional Economic Information DVD,
http://www.bea.gov/regional/docs/reis2008dvd.cfm.
10
importance of government in a metropolitan area by the number of people employed by
local, state and federal governments, as a fraction of total employment in the MSA.
Also, it is argued that some of the ancillary (but hard-to-measure) benefits of
“green” building, such as improved employee productivity through superior indoor air
quality, may benefit the space-intensive service sector in particular. (See Piet M.A.
Eichholtz, et. al, 2010b, for a discussion.) We define the importance of the service sector
as the aggregate number of jobs in “financial activities,” “professional and business
services,” “information,” and “other services,” as a fraction of total employment in the
MSA.13
Property Market Conditions. The characteristics of the property market surely
affect the diffusion of energy efficiency technologies in building. First, as noted above,
several studies have documented a price premium for office space certified by the EPA or
by the USGBC. However, there seems to be an inverse relation between location rents
and the size of this premium. This may suggest that the signal conveyed by energy labels
is more valuable in markets with weaker fundamentals (Piet M.A. Eichholtz, et al.,
2010a).
Second, we expect that the adoption of Energy Star and LEED certificates is
positively related to new construction in a metropolitan area, as local building codes and
federal energy efficiency requirements progress and as building technology makes
investments in energy efficiency more attractive. New construction starts are dependent
on the stage of the property cycle, i.e., upon market fundamentals such as the vacancy
rate and rental levels.
Finally, the adoption of energy efficiency in commercial real estate may also be a
function of size, as suggested strongly by the findings on technology diffusion in other
industries (Lori D. Snyder, et al., 2003).
We measure the characteristics of the local property market by: the total office
stock, the average vacancy rate (vacant space as a percentage of the property type), the
average rental price (that is, the lease for office space in the average building, corrected
for the hedonic characteristics of properties), and the average property price (estimated
13
Bureau of Labor Statistics, NAICS based Quarterly Census of Employment and Wages (QCEW) data,
ftp://ftp.bls.gov/pub/special.requests/cew/. We use the definitions of these sectors as
provided by NAICS.
11
for a 100,000 square foot building and derived from the average rent, the vacancy rate
and the prevailing capitalization rate in the MSA).14
Building Professionals. The design and construction of energy efficient
commercial space requires specific technical knowledge, supplied by architects and
engineers among others. In fact, the human infrastructure developed around the LEED
program is quite substantial; as of November 2010, more than 150,000 designers,
contractors, and consultants had earned the designation “LEED Accredited Professional”
(LEED AP). We measure the availability of “human capital” by the number of LEED
APs registered by MSA and year and by the number of architecture graduates within the
MSA from schools with NAAB-accredited programs.15 The pool of local experts –
building professionals and those specifically trained in sustainable technology – may help
overcome one of the most important barriers to diffusion identified by Bronwyn Hall
(2003): the lack of professional or business channels to acquire specific information
about an innovation, its cost, its technical properties, its likely impact on productivity.16
Political Ideology. There is a growing literature on the role of ideology in
consumer choice. In particular, there is strong evidence that “green” consumers are
predisposed to adopt environmental innovations, and that they are more responsive to
energy conservation “nudges” (Matthew E. Kahn, 2007; Dora Costa and Matthew E.
Kahn, 2010a). In a similar spirit, one may expect political ideology to influence the
adoption of energy efficiency and “green” technologies in commercial building across
US metropolitan areas. We measure the dominant political preferences in each MSA by
the percentage vote for Ronald Reagan in 1984 and the percentage vote for George H.W.
Bush in 1988 by MSA.17
Regulation and Incentives. Government policies, such as regulation and
incentives, may also play an important role in explaining the growth in adoption of
energy efficient innovations (See Adam B. Jaffe and Karen Palmer, 1997). Some cities,
counties and states have adopted specific policies to stimulate “green” construction. The
14
All of these data were obtained from CBRE Econometric Advisors.
See editions 5-8 of the Guide to Architecture Schools, Washington, DC: Association of Collegiate
Schools of Architecture Press.
16
See Green Building Certification Institute, http://www.gbci.org/, and National Architecture Association
Board, http://www.naab.org/.
17
See CQ Press Electronic Library, http://library.cqpress.com/elections/export.php.
15
12
palette is diverse, and policies range from “fast-tracking” building permits for LEEDcertified developments, to subsidies and tax credits for energy-efficiency innovations, to
specified minimum LEED performance standards. The US Green Building Council
registers policies related to “green” building, at the city, county, and state level, even
though it does not distinguish between the importance or the type of policy.18 We
construct a simple measure of the “intensity” of green-building-related policies by
aggregating LEED-related policies by MSA by year.
B. Cross-Sectional Evidence on the Diffusion of Energy Efficiency
We first relate the 2010 cross-section measures of the diffusion of energyefficient technology -- the fraction of space certified by Energy Star or LEED -- to lagged
values of the various measures of local economic conditions. Figure 3 presents a series of
scatter plots reporting the bivariate relation between these variables and the adoption of
sustainable building technologies. The figures indicate the cross-sectional variation in the
diffusion of Energy-Star-certified space and LEED-certified space, as a fraction of the
total office stock, as well as a fitted regression line.
Panel A presents measures of general economic conditions and industry
composition. Both measures of personal income are positively related to the adoption of
energy-efficient (Energy Star) and sustainable (LEED) construction practices. The scatter
diagrams are consistent with empirical evidence reporting the positive association
between income and the willingness to pay for environmental goods (Brian Roe, et al.,
2001) and the correlation between income and the support for public environmental
spending (Euel Elliott, Barry J. Seldon and James L. Regens, 1997).
If higher incomes are related to value-added per employee, companies may also
be more like to adopt “green” space -- it is claimed that firms demonstrating a positive
attitude towards the natural environment are considered more attractive employers than
otherwise comparable firms without such a demonstrated attitude (Talya N. Bauer and
Lynda Aiman-Smith, 1996). As human capital is increasingly viewed as a key source of
value creation in modern firms (Luigi Zingales, 2000), “green” real estate may be a
18
See http://www.usgbc.org/PublicPolicy/SearchPublicPolicies.aspx?PageID=1776.
13
visible signal of a firm’s environmental policy, and might thus contribute to a firm’s
success in attracting better workers.
This argument may also explain the positive relation between the share of jobs in
the service sector and the adoption of energy efficiency in commercial buildings, reported
in the lower-left graph of Figure 3A.
The importance of the government in the metropolitan job market seems unrelated
to the adoption of LEED-certified space, and also to the adoption of energy efficient
technologies in the office market. The scatter diagram suggests that in markets where the
government is a more important tenant, the average observable energy efficiency of
buildings is lower.19
Panel B shows the bivariate relation between climatic conditions and the adoption
of Energy Star and LEED certificates across MSAs. One would expect the economic
payoff from energy efficiency investments to be positively related to heating and cooling
degree days, but surprisingly, the energy efficiency of building technology seems
unrelated to more challenging climatic circumstances.
In Panel C, local property market characteristics are related to the diffusion of
energy efficiency in commercial buildings. Larger property markets have higher fractions
of energy-efficient and “green” space, and the average volume of commercial property
available is positively related to the diffusion of Energy Star and LEED certificates. The
effect of size may be associated with distributing fixed costs over a wider base, and larger
(absolute) payoffs of energy efficient improvements -- as suggested in the literature on
firm size as a determinant of technology diffusion (Stephen J. DeCanio and William E.
Watkins, 1998, Nancy L. Rose and Paul L. Joskow, 1990). Larger size may also be
associated with lower costs in the diffusion of technical knowledge, as suggested by
Bronwyn H. Hall (2003).
The adoption of energy efficient and “green” technology in building is positively
related to average office rents and values. These results are an indication that investments
in energy efficient technologies are more likely under more favorable property market
fundamentals. We note that the relation between rents and values, and technology
19
Note, however, that a large share of space occupied by local, state and federal government is in illiquid,
owner-occupied buildings, which are not reported in our data.
14
adoption rates may also be quite spurious, as unobservables such as age and building
quality are positively related to rents, prices, and the adoption of energy labels.
Panel D presents the simple correlation between commercial electricity prices in
local markets and the diffusion of technologies resulting in Energy Star and LEED
certification. There is a strong and positive relation between commercial electricity prices
and the adoption of energy efficient technologies, which is consistent with conventional
investment theory -- the economic return to energy efficient investments improves with
higher energy prices, ceteris paribus. A case in point is Hawaii, the outlier at the top-right
of the graph. Electricity prices in Hawaii are consistently the highest in the country, and
building owners in Hawaii have expended considerable efforts in reducing energy
consumption in commercial buildings.
Note that we find practically no relation between energy prices and the adoption
of “sustainable” building standards. The LEED standard signals more than energy
efficiency alone -- if it measures energy efficiency at all (Guy R. Newsham, et al., 2009;
John H. Scofield, 2009). Alternatively, there may be sufficiently strong non-financial
utility for property investors and tenants in adopting sustainable building standards, as
suggested by the thesis on private provision of public goods. (See Matthew J. Kotchen,
2006, for a discussion.)
Panel E indicates the dispersion of energy efficient buildings and the presence of
trained building professionals across MSAs. Both Energy Star and LEED certification are
positively related to the number of architects and engineers that are officially accredited
by the Green Building Certification Institute (GBCI) -- this form of professional training
is specific to “green” building technology. (Alternatively, it is certainly possible that this
skilled labor simply sorts into places where demand and compensation for the specific
skill is highest.)
In Panel F, we relate political preferences in the metropolitan area to the adoption
of energy efficient technology in commercial buildings. Our measures of ideology, the
percentage vote for Reagan in 1984 and the percentage vote for Bush in 1988, are
negatively related to the adoption of environmental technologies. This finding confirms
recent research on consumer choice and environmental ideology. This research has
documented the extent to which political preferences are moderating factors in the
15
effectiveness of energy efficiency outreach programs by utilities (Dora L. Costa and
Matthew E. Kahn, 2010a), in general perceptions of environmental issues (Matthew E.
Kahn and Matthew J. Kotchen, 2010), and in the actual “green” consumption behavior of
consumers (Matthew E. Kahn, 2007).
Panel G relates “green” building policies to the adoption of energy efficiency and
“sustainability” in commercial real estate. Quite clearly, there is a positive correlation
between policies and the diffusion of “green” space – public regulation and incentives
stimulate more energy-efficient building. This is consistent with the work of Adam B.
Jaffe and Karen Palmer (1997), who document a positive effect of environmental
compliance on R&D expenditures (offering some support for the “Porter hypothesis” on
environmental regulation and economic performance). Interestingly, our proxy for
environmental regulation and policies is also associated with higher fractions of energyefficient space, even though the measure is based only on LEED-related policies.
Of course, all these inferences above are drawn from a perilously small sample -48 observations on U.S. metropolitan property markets observed in one year – and they
ignore the presence of other observables that may explain the variation in the adoption of
energy efficiency in building.
C. Dynamic Evidence on the Diffusion of Energy Efficiency
We exploit the dynamics in the dispersion of energy-efficient office space across
metropolitan areas. First, we model the dynamic relationship between the diffusion of
labeled office space over time and geographical markets in a straightforward manner:
(1) Fraction it = α + βX it−2 + εit ,
where Fraction it is the fraction of certified office space, X it−2 is a vector of
metropolitan incomes, energy prices, and property market characteristics. We use a twoyear lag of the explanatory variables to account for the real time necessary to complete
property renovations and new property development. To address the fact that the pattern
of diffusion of energy efficiency and “sustainability” in buildings is highly
16
autocorrelated, we estimate equation (1) using a simple model of first order serial
correlation, AR(1), estimated efficiently by Feasible Generalized Least Squares (FGLS).
Second, we model the dispersion of energy-efficiency labels across time and
space using first differences, which controls for time-invariant unobserved effects
specific to MSAs:
(2) ΔFraction it = α + βΔXit−2 + εit
Third, to account for possible endogeneity of the independent variables, we report
more general results following the Arellano-Bond (1991) procedure, where all covariates
are instrumented by their own lagged values in a GMM estimation.
Table 1 summarizes the relationship between the diffusion of energy-efficient
office space and a few presumed key economic determinants of the adoption of energyefficient buildings: income, energy prices, and a crude summary of property market
characteristics. Panel A presents predictions about the diffusion of Energy Star
certification across the 48 MSAs; Panel B presents predictions about the diffusion of
LEED certification. Columns (1) and (2) report the relationship in levels; the dependent
variable is the fraction of space certified as energy efficient by MSA and year, Columns
(3) and (4) present the same models in first differences (to control for time invariant
unobservables in the diffusion of energy labels in the office sector), and Columns (5) and
(6) report more general results using Arellano-Bond GMM estimation.
Panel A shows that income is important in explaining the diffusion of Energy-Star
certified buildings over space and time. In areas with higher income levels and stronger
income growth adoption of energy-efficient building practices, as reflected in the fraction
of labeled space, is significantly higher. In five of the six regressions explaining the
diffusion of Energy-Star certification over space, the price of commercial electricity is
highly significant, with an estimated price elasticity of about 0.6. The measure of the
relative size of the property market is significant in two of the six specifications, but
coefficients are ambiguous.
Surprisingly, the results documented in Panel B suggest that the price of energy is
essentially irrelevant to the geographical and temporal variation in the diffusion of
17
LEED-certified office space. The measures of income are significant in four of the six
specifications, and the diffusion of LEED certification appears to be highly income
elastic. The measure of property market conditions is significant in the first-differences
models only -- in markets with a larger supply of office space per employee in the service
sector, the adoption of energy efficient technologies is higher.
These differences in the regression results may be implied by differences in the
criteria employed for the award of Energy Star and LEED certification. Energy Star
certification is based only upon energy efficiency in building operation; this is clearly
more important in property markets in which the price of energy is higher. LEED
certification is based on a variety of aesthetic features of building, and energy efficiency
is of lesser importance. These features are apparently more important in metropolitan
areas where incomes are higher, which may be related to the positive association between
income and the willingness to pay for environmental goods (Brian Roe, et al., 2001).
Also, the ancillary benefits of LEED-certification may be more valuable in areas where
incomes, and thus the average value-added per employee, are higher. Energy prices are
not particularly important in explaining the cross-sectional diffusion of LEED-labeled
buildings.
Table 2 presents a series of models in which several additional variables are
included as regressors. The variable measuring personal income is excluded from these
models, because its strongly related to some of the other variables. (Likewise, correlation
across covariates inhibits estimation of fully specified models.) Columns (1) through (4)
report results for the diffusion of Energy-Star-certified space, and Columns (5) through
(8) report results for the adoption of LEED-certification in office buildings.20
Column (1) provides some evidence that Energy Star certification has increased in
markets with lower unemployment rates. Higher demand for office space, leading to
more favorable conditions in the property market (and more new construction), clearly
affects the diffusion of energy efficient technologies in building. This is also reflected in
the positive and significant (albeit weakly significant) coefficient for the share of service
sector jobs in the local economy -- more white-collar jobs means higher demand for
20
Results are reported for linear GMM models only. Logarithmic results and results from GLS estimations
accord largely with the findings reported here, and are available upon request.
18
office space and higher adoption rates of energy efficient technologies in the commercial
office sector across space and time.
Of course, we can also measure the conditions in the commercial property market
directly. Column (2) includes the (lagged) vacancy rate and average property values
across MSAs and over time. The adoption of energy-efficient and “green” building
practices is more likely in more healthy property markets, with lower vacancy rates and
higher average property values. The expected payoff from investments in energy
efficiency increases with lower volatility in occupancy rates, and the dollar amount of the
value increment that “green” buildings may command in the marketplace is more
significant if property prices are higher. Naturally, lower vacancy rates will also trigger
new construction, which may also increase the fraction of “green” space.
In column (3), we evaluate the impact of climatic conditions and building
professionals on the diffusion of Energy Star certification. The energy efficiency of
building technology is unrelated to more extreme climatic circumstances, even though the
return to energy efficiency investments is expected to be positively related to heating and
cooling degree days. The presence and growth of “human capital” is negatively related to
the diffusion of energy efficient space. Our proxy measures the number of LEED
Accredited Professionals (standardized by total population), and this certification is
apparently unrelated to engineering knowledge on energy efficiency in commercial
buildings.
Column (4) relates the presence of LEED-related policies to the adoption of
energy efficiency innovations, but there is no evidence of spillover effects of these
specific regulations and incentives leading to more efficient building performance.21
Columns (5) through (8) present similar models to explain the diffusion of LEEDcertified buildings across space and time. In common with the analysis for Energy Star,
the adoption of LEED certification seems to be a consequence of income and property
market fundamentals. Areas with higher wages and salaries have higher levels and
stronger growth in “green” construction or retrofits. Higher vacancy rates and lower
property values hamper to the diffusion of “green” building innovations.
21
There is an extensive discussion of reasons for inclusion of LEED performance standards in conventional
building codes. But the voluntary LEED system was never intended to serve as government code.
19
Importantly, the number of building professionals trained to perform LEED audits
has a positive effect on the growth of green space, as reported in Column (7). This
finding supports the notion that the presence of professional or business channels to
acquire specific information about an innovation and its technical properties is one of the
most important determinants of technology diffusion (Bronwyn Hall, 2003). Also, local
policies designed to stimulate more “sustainable” building practices have a significantly
positive effect on the diffusion of LEED-certified space, although we cannot distinguish
between the effectiveness of regulations or other incentives.
IV. Conclusions
Despite much discussion about the “energy paradox” in the built environment, the
diffusion of energy efficiency and “sustainability” in building technology has been
widespread and rapid. This paper documents this diffusion over time and across U.S.
property markets. By 2010, about thirty percent of all commercial office space in the 48
largest metropolitan areas was certified for energy efficiency by Energy Star. About
eleven percent of office space was certified as sustainable by LEED. But there is
considerable variation across metropolitan areas. In Los Angeles, for example, more than
half of all commercial office space has been certified for energy efficiency.
The diffusion has been more rapid in metropolitan areas with higher incomes, and
in those with sound property market fundamentals (for example, lower vacancy rates and
higher property values). These findings have implications for the property markets across
the US that are faced with more dire economic conditions, such as Dallas, Detroit and
Tampa; these areas will lag behind in the energy efficiency of their commercial office
stock.
Importantly, the diffusion of energy efficient technology in buildings is more
responsive to energy prices than is the diffusion of buildings certified for “sustainability.”
Commercial property markets -- and, more specifically, building owners -- seem to
evaluate the impact of resource consumption upon the profitability of investment in real
capital. This lends considerable support to the efficiency of energy investment decisions
in the business sector, certainly compared to the “energy paradox” decried in the
residential sector.
20
Finally, the diffusion of “green” space is facilitated by factors such as trained
building professionals and governmental policies. LEED policies and the LEED
professional education program seem to be effective in stimulating the growth of “green”
space, but the consequences of this growth on the built environment remains unclear.
21
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24
Figure 1
LEED and Energy Star Dynamics
(1995-2010)
A. Extent of Certified Office Space
B. Fraction of Certified Office Space
25
Figure 2
LEED and Energy Star Dynamics
Fraction of Certified Office Space Across US MSAs
(1995-2010)
A. New York
B. Los Angeles
C. Chicago
26
D. Washington
E. Dallas
F. Phoenix
27
Figure 3
Correlates of the Diffusion of Energy Efficiency in Building, 2010
A. Income and Employment
B. Climatic Conditions
28
C. Property Market Characteristics
D. Energy Prices
29
E. Building Professionals
F. Ideology
G. Government Policies on Green Building
30
Table 1
Basic Regression Results
(dependent variable: fraction of certified commercial office space)
A. Energy Star
Income
($ thousands)
Average Electricity Price
($ per kWh)
Office Space/Worker
(sq. ft.)
Fraction Rated Spacet-1
Constant
Observations
Wald Chi2
AR(1) Coefficient
Sargan Test
Levels
linear
log-log
0.005***
4.750***
[0.001]
[0.273]
0.913***
0.558***
[0.133]
[0.166]
0.000
-0.238***
[0.000]
[0.084]
-0.357***
[0.041]
768
72.30
1.022
-
First Differences
linear
log-log
0.005***
3.395***
[0.001]
[0.710]
0.538***
0.105
[0.113]
[0.174]
-0.000
0.037
[0.000]
[0.032]
-
-
-50.430***
[3.021]
493
396.8
0.837
-
0.002
[0.002]
768
63.25
0.398
-
-0.080
[0.166]
445
24.29
0.064
-
Arellano-Bond
linear
log-log
0.003***
3.450***
[0.000]
[0.388]
0.317**
0.623***
[0.140]
[0.192]
0.000**
-0.021
[0.000]
[0.072]
-0.135***
-36.466***
[0.017]
[4.424]
0.898***
0.409***
[0.025]
[0.045]
768
397
7,842
4,376
320.0
127.5
B. LEED
Income
($ thousands)
Average Electricity Price
($ per kWh)
Office Space/Worker
(sq. ft.)
Fraction Rated Spacet-1
Constant
Observations
Wald Chi2
AR(1) Coefficient
Sargan Test
Levels
linear
log-log
-0.003***
8.726***
[0.000]
[0.903]
-0.027
0.416
[0.046]
[0.456]
-0.000
-0.111
[0.000]
[0.160]
0.064***
[0.008]
768
136.4
1.145
-
First Differences
linear
log-log
-0.000
1.252
[0.000]
[2.787]
-0.050
-0.584
[0.033]
[0.641]
-0.000**
-0.213**
[0.000]
[0.098]
-
-
-95.938***
[10.192]
290
141.3
0.843
-
0.005***
[0.001]
768
7,311
0.753
-
31
1.697***
[0.514]
239
5,607
0.095
-
Arellano-Bond
linear
log-log
0.001***
10.112***
[0.000]
[1.016]
0.123
1.070
[0.099]
[0.794]
-0.000
-0.340
[0.000]
[0.239]
1.223***
0.307***
[0.042]
[0.045]
-0.055***
-106.616***
[0.010]
[11.698]
768
194
1,533
952.1
245.5
92.50
Table 2
Arellano-Bond GMM Regression Results
(dependent variable: fraction of certified commercial space)
Energy Star
Unemployment Rate
(percent)
Share of Government Jobs
(percent)
Share of Service Sector Jobs
(percent)
Commercial Vacancy Rate
(percent)
Average Commercial Property Value
($ million)
Cooling Degree Days
(thousands)
Heating Degree Days
(thousands)
LEED Accredited Professionals
(Share of total population)
Local Policies Encouraging LEED
(count)
Average Electricity Pricet
($ per kWh)
Office Space/Worker
(sq. ft.)
Constant
Observations
Wald Chi2
Sargan Test
(1)
-0.631***
[0.113]
-0.070
[0.049]
0.097*
[0.057]
(2)
LEED
(3)
(4)
(5)
-0.269***
[0.074]
-0.016
[0.031]
0.022
[0.036]
-0.001*
[0.000]
0.002***
[0.001]
(6)
768
6,648
307.0
0.358**
[0.151]
0.024*
[0.013]
-0.031**
[0.014]
749
6,421
309.7
(8)
-0.000**
[0.000]
0.001***
[0.000]
0.005
[0.008]
-0.009
[0.008]
-60.783*
[33.691]
0.421***
[0.147]
0.050***
[0.018]
-0.016
[0.024]
(7)
0.395**
[0.189]
0.028*
[0.015]
-0.019
[0.024]
473
3,894
209.9
0.007**
[0.004]
0.000
[0.004]
117.362***
[23.366]
0.001
[0.001]
0.437***
[0.148]
0.025**
[0.012]
-0.026**
[0.011]
768
6,487
327.7
32
0.260**
[0.101]
0.001
[0.011]
-0.008
[0.016]
768
1,258
245.3
0.208**
[0.102]
-0.006
[0.008]
-0.021**
[0.010]
749
1,290
237.4
0.100
[0.105]
-0.005
[0.007]
-0.020
[0.012]
473
1,144
164.8
0.003***
[0.000]
0.174*
[0.095]
-0.004
[0.007]
-0.011
[0.008]
768
1,590
242.7
Appendix Table 1A
Fraction of Certified Office Space Across US MSAs
(2010)
A. Energy-Star-Certified Office Space
Total Office
Stock
Metropolitan Area
New York- New Jersey-Long Island
Washington-Arlington-Alexandria
Los Angeles-Long Beach-Santa Ana
Chicago-Joliet-Naperville
Dallas-Fort Worth-Arlington
Boston-Cambridge-Quincy
Houston-Sugar Land-Baytown
San Francisco-Oakland-Fremont
Atlanta-Sandy Springs-Marietta
Philadelphia-Camden-Wilmington
Miami-Fort Lauderdale-Pompano Beach
Denver-Aurora-Broomfield
Seattle-Tacoma-Bellevue
Phoenix-Mesa-Glendale
Pittsburgh
Detroit-Warren-Livonia
Minneapolis-St. Paul-Bloomington
Baltimore-Towson
San Diego-Carlsbad-San Marcos
Raleigh-Cary
Kansas City
Sacramento-Arden-Arcade-Roseville
Charlotte-Gastonia-Rock Hill
St. Louis
Portland-South Portland-Biddeford
Number of
Energy-Star-Certified
Buildings
Fraction of
Energy-Star-Certified
Buildings
188
295
593
185
165
121
214
313
142
76
96
176
105
86
17
47
119
19
105
12
24
110
56
17
3
7.51
13.18
23.61
11.85
9.84
7.51
21.75
21.47
9.81
4.92
6.72
15.15
10.21
5.34
1.07
6.85
27.80
2.57
9.04
1.03
3.05
11.09
11.18
3.22
0.40
(million sq.ft.)
554.73
294.85
251.31
224.40
181.60
161.65
147.88
142.15
138.92
118.56
100.44
93.48
82.85
77.82
76.74
69.86
65.35
57.56
57.25
49.95
49.14
44.70
44.34
43.49
43.22
33
Energy-Star-Certified
Space
(million sq.ft.)
109.32
95.92
135.09
100.02
63.07
40.68
86.78
84.21
51.36
24.29
19.93
43.31
32.09
18.06
4.60
17.15
49.59
4.52
15.41
1.76
4.70
15.20
10.32
5.20
0.04
Fraction of
Energy-Star-Certified
Space
19.71
32.53
53.76
44.57
34.73
25.16
58.68
59.24
36.97
20.49
19.84
46.33
38.73
23.20
5.99
24.55
75.88
7.85
26.92
3.52
9.57
34.00
23.27
11.96
0.08
Appendix Table 1A (continued)
Fraction of Certified Office Space Across US MSAs
(2010)
B. LEED-Certified Office Space
Total Office
Stock
Metropolitan Area
New York- New Jersey-Long Island
Washington-Arlington-Alexandria
Los Angeles-Long Beach-Santa Ana
Chicago-Joliet-Naperville
Dallas-Fort Worth-Arlington
Boston-Cambridge-Quincy
Houston-Sugar Land-Baytown
San Francisco-Oakland-Fremont
Atlanta-Sandy Springs-Marietta
Philadelphia-Camden-Wilmington
Miami-Fort Lauderdale-Pompano Beach
Denver-Aurora-Broomfield
Seattle-Tacoma-Bellevue
Phoenix-Mesa-Glendale
Pittsburgh
Detroit-Warren-Livonia
Minneapolis-St. Paul-Bloomington
Baltimore-Towson
San Diego-Carlsbad-San Marcos
Raleigh-Cary
Kansas City
Sacramento-Arden-Arcade-Roseville
Charlotte-Gastonia-Rock Hill
St. Louis
Portland-South Portland-Biddeford
Number of
LEED-Certified
Buildings
(million sq.ft.)
554.73
294.85
251.31
224.40
181.60
161.65
147.88
142.15
138.92
118.56
100.44
93.48
82.85
77.82
76.74
69.86
65.35
57.56
57.25
49.95
49.14
44.70
44.34
43.49
43.22
138
186
133
140
73
111
76
143
91
71
37
79
105
48
30
17
58
43
43
7
20
35
45
32
7
34
Fraction of
LEED-Certified
Buildings
5.51
8.31
5.29
8.97
4.36
6.89
7.72
9.81
6.29
4.59
2.59
6.80
10.21
2.98
1.90
2.48
13.55
5.82
3.70
0.60
2.54
3.53
8.98
6.06
0.93
LEED-Certified Space
(million sq.ft.)
27.86
31.84
35.63
41.57
19.76
23.95
29.82
27.46
18.52
8.38
5.81
16.04
26.03
9.29
2.40
1.36
14.01
6.99
6.89
0.65
3.13
9.34
6.99
2.65
0.16
Fraction of
LEED-Certified Space
5.02
10.80
14.18
18.53
10.88
14.82
20.16
19.32
13.33
7.07
5.79
17.16
31.42
11.94
3.12
1.94
21.44
12.14
12.03
1.30
6.38
20.90
15.77
6.08
0.37